Distributed Algorithms for Optimal Decision-Making

News

James’ article on individual confidence in collective decisions, with Gavin Brown (Manchester) and Andy Radford (Bristol), is the cover featured article for the September issue of Trends in Ecology and Evolution.

A new Opinion on individual confidence and collective decision-making is in press in Trends in Ecology and Evolution, authored by James together with Andy Radford (Bristol) and Gavin Brown (Manchester).

The Opinion argues for the consideration of subjective confidence and its influence on communication within collectively-deciding groups. The Opinion also draws links between confidence by individually-optimal decision-makers, and the optimal confidence-based weighting scheme for group decisions.

Our recent paper on decision making in honeybees has been selected to be an Editor’s Suggestion in Physical Review E. The journal prominently lists a small number of Physical Review E papers that the editors and referees find of particular interest, importance, or clarity. Here is alink to the paper. You can also find the paper in the Publications section.

James and project collaborator Naomi Leonard (Princeton) co-organised a minisymposium on ‘Excitability, Feedback and Collective Decision-Making Dynamics’, at the 2017 SIAM Meeting on Dynamical Systems, in Snowbird.

Thomas contributed one of four talks on decision-making dynamics, exploring the roles of excitability and feedback in neural and collective decision systems. The speakers were:

The video above showcases the functionalities of ARK through three demos. In Demo A, ARK automatically assigns unique IDs to a swarm of 100 Kilobots. Demos B shows the possibility of employing ARK for the automatic positioning of 50 Kilobots, which is one of the typical preliminary operations in swarm robotics experiments. These operations are typically tedious and time consuming when done manually. ARK saves researchers’ time and makes operating large swarms considerably easier. Additionally, automating the operation gives more accurate control of the robots’ start positions and removes undesired biases in comparative experiments. Demo C shows a simple foraging scenario where 50 Kilobots collect material from a source location and deposit it at a destination. The robots are programmed to pick up one virtual flower inside the source area (green flower field), carry it to the destination (yellow nest), and deposit the flower there. When performing actions in the virtual environments, the robot signals by lighting its LED in blue. When picking up a virtual flower from the source, the robot reduces the source’s size for the rest of the robots (by reducing the area’s diameter by 1cm). Similarly when a robot deposits flowers at its destination, the area increases by 1 cm. This demo shows that robots can perceive (and navigate) a virtual gradient, can modify the virtual environment by moving material from one location to another, and can autonomously decide when to change the virtual environment that they sense (either the source or the destination).
More information available at: http://diode.group.shef.ac.uk/kilobots/index.php/ARK

The DiODe team organises a minisymposium at the Mathematical Models in Ecology and Evolution conference taking place in London this July. In this minisymposium, recent progress on collective behaviour and decision making will be discussed by a selection of excellent speakers.

Two new papers with results of the DiODe project have been accepted recently. The review article entitled Collective Decision Making, which appeared in the journal Current Opinion in Behavioural Sciences, summarises recent progress in natural and artificial collective decision making. The other paper entitled A model of the best-of-N nest-site selection process in honeybees has been accepted for publication in Physical Review E and generalises in a theoretical study the nest site selection of honeybees to three and more options.

Salah Talamali joins the DiODe team beginning of May 2017 to investigate heterogeneities in collective decision making. His PhD project will involve the development of decision making algorithms and their implementation on the Kilobot platform, bringing the state of the art of artificial decision making closer to studying real-world scenarios using a swarm of robots.